Taylor, Steven, 2019 The lung microbiome in chronic airway disease: determinants and clinical implications, Flinders University, College of Medicine and Public Health

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Abstract

Chronic diseases of the lower airways are a leading cause of global morbidity and mortality. The current approach to the management of these patients relies on diagnostic labels based on criteria including clinical history, environmental exposures, and physiology. While these criteria help us to understand disease aetiology, they poorly describe the substantial interpersonal variation in disease trajectory and response to treatment. As most symptoms arise from the lower airways, an emerging approach to improve patient stratification is to precisely characterise the lower airway environment in a clinically informative manner. The pathophysiology of this environment is determined by complex disease traits, such as the degree and type of airway inflammation, mucus secretion, and microbial colonisation. However, the interactions between these traits, and how they reflect and contribute to lung pathophysiology are poorly understood.

With advances in sequencing technology, the improved ability to measure the lower airway microbiota can identify not only pathogenic organisms that contribute to disease, but also compositional characteristics of the microbiota that reflect the selective conditions of the airways. However, it is unknown whether microbiota analysis can provide insight into the complex lower airway environment and stratify patients in a clinically informative manner. It is also unknown what lower airway determinants select the microbiota and how this affects disease. This dissertation aims to explore these unknowns by measuring the effect of determinants of the lower airway environment on the microbiota composition and assessing how this correlates with clinical markers of disease.

Firstly, the selective effect of airway inflammation is explored in patients with persistent uncontrolled asthma. Neutrophilic inflammation, but not eosinophilic inflammation, was found to select a microbiota composition that has a low diversity and a high relative abundance of taxa considered pathogenic. Secondly, the selective pressure of mucus composition is examined, where variation to mucus sugar expression is explored in relation to the lower airway microbiota in patients with bronchiectasis. Patients who display versatile sugar groups in mucosal secretions were found to select a microbiota dominated by pathogenic organisms, with important clinical consequences. Thirdly, the selective pressure of pharmaceutical treatment is assessed, through exploring the effect of long-term macrolide treatment on antibiotic resistance gene carriage and microbiota composition. To measure broad changes in resistance gene carriage, a novel shotgun metagenomic sequencing method was developed and tested. This identified that macrolides increase the carriage of both macrolide and tetracycline resistance genes. Through assessment of the selective effect of macrolides on microbiota composition, it was found that macrolides reduce microbiota diversity and the abundance of a key airway pathogen.

Together, the results of this dissertation demonstrate the potential clinical value of microbiota analysis to assess the characteristics of the lower airway environment. The selective pressures of airway inflammation type, mucosal sugar presentation, and macrolide treatment have profound effects on the airway environment and can contribute to disease through their ability to influence the composition of the airway microbiota. These findings represent important steps towards a precision medicine approach based on knowledge of an individual’s disease characteristics.